Perceiving the motion of objects in our environment is critical to many aspects of daily life. Although much is known about the neural basis of visual motion perception, almost all of that knowledge is derived from experiments in which the observer's eyes and head remain stationary. When an observer is moving through the environment, the motion of an object on the retina depends on both the movement of the object in the world and the self-motion of the observer. Thus, to estimate the motion of objects in the world, self-motion must also be estimated and factored into the computation. This capability is critical to daily activities such as driving a vehicle, during which it is important to accurately judge the movement of other objects in the environment, such as other vehicles or pedestrians. As perception of self-motion is known to be degraded in neurological disorders such as Alzheimer's disease and during normal aging, understanding the neural basis of object motion perception during self-motion is of substantial importance to developing therapies for these deficits. To begin to study where and how neural circuits compensate for self-motion and compute the motion of objects in the world, we seek to develop a macaque model of object motion perception during self-motion. This endeavor poses some serious technical and analytical challenges, thus we seek an Exploratory/Developmental Research grant to develop an effective animal model to study this problem. Aim #1 seeks to develop a behavioral paradigm in which macaques are trained to judge object motion during self-motion, and to report their percepts in world-centered or retinocentric coordinates. Visual and vestibular cues to self-motion will be presented separately and together to determine the contribution of vestibular signals to this task. Our hypothesis is that vestibular signals contribute importantly to computing object motion in world coordinates. Aim #2 seeks to record from single neurons in area MST during performance of the object motion discrimination task. By analyzing single-unit responses and decoding population responses, we will explore the neural mechanisms by which retinal image motion is transformed into a representation of object motion in the world.